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Common Agricultural Policy Regional Impact – The Rural Development Dimension
Collaborative project - Small to medium-scale focused research project under the Seventh Framework Programme
Project No.: 226195
WP4 Baseline
Deliverable: D4.8
Management guidelines for the CAPRI
baseline
Mihaly HIMICS, Pavel CIAIAN, Benjamin VAN DOORSLAER, Guna SALPUTRA
Partner(s): JRC-IPTS
Final version: 22.02.2013
The views expressed are purely those of the authors and may not in any circumstances be regarded
as stating an official position of the European Commission.
The FP7 project "Common Agricultural Policy Regional Impact – The Rural Development Dimension"
(CAPRI-RD) aims at developing and applying an operational, Pan-European tool including all
Candidate and Potential Candidate countries to analyse the regional impacts of all policy measures
under CAP Pillar I and II across a wide range of economic, social and environmental indicators. The
project is carried out by a consortium of 10 research organisations, led by Bonn University (UBO).
Authors of this report and contact details
Names: Mihály HIMICS, Pavel CIAIAN, Benjamin VAN DOORSLAER,
Guna SALPUTRA
Partner acronym: JRC-IPTS
Address: European Commission - Joint Research Centre (JRC)
Institute for Prospective Technological Studies (IPTS)
Edificio Expo, C/ Inca Garcilaso 3, E-41092 Sevilla, Spain
E-mail: pavel.ciaian@ec.europa.eu
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Table of contents
ABBREVIATIONS ...................................................................................................... 5
1. INTRODUCTION ................................................................................................. 6
2. OVERVIEW OF THE CAPRI MODELLING SYSTEM ......................................... 7
3. CAPRI BUILDING BLOCKS ............................................................................... 9
4. BUILDING A CONSISTENT DATASET FROM DIVERSE STATISTICAL
SOURCES ................................................................................................................ 11
5. BASELINE PROCESS OF DG AGRI ................................................................ 13
6. PROJECTIONS OF THE FUTURE STATE OF THE ECONOMY ..................... 15
6.1. CAPTRD ................................................................................................................................................ 15
6.2. Projections of global commodity balances .......................................................................................... 16
7. CALIBRATION OF THE CAPRI MODELLING SYSTEM ................................. 18
8. VALIDATION OF THE CAPRI BASELINE RESULTS...................................... 20
9. GOOD MANAGEMENT PRACTICES FOR THE BASELINE ........................... 21
10. CONCLUDING REMARKS ........................................................................... 22
REFERENCES ......................................................................................................... 25
ANNEX 1: CALIBRATION STEPS .......................................................................... 27
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Abbreviations
AGLINK- COSIMO
Recursive-dynamic, Partial Equilibrium, Supply Demand Model of World Agriculture, Developed by the OECD Secretariat in Close Co-operation with Member Countries and Certain Non Member Economies
AGMEMOD Agricultural Member State Modelling for the EU and Eastern European Countries
AMAD Agricultural Market Access Database CAP Common Agricultural Policy CAPRI Common Agricultural Policy Regional Impact cif Cost, Insurance and Freight CLC Corine Land Cover COMEXT Intra- and extra-EU Trade Data COSIMO Commodity Simulation Model DG-AGRI Directorate General for Agriculture and Rural Development EAA Economic Accounts for Agriculture EBB European Biodiesel Board ePURE European renewable ethanol ESIM European Simulation Model FAO Food and Agriculture Organization FAOSTAT Statistics Division of the FAO FAPRI Food and Agricultural Policy Research Institute fob Free on Board FSS Farm Structure Survey GDP Gross Domestic Product GLOBIOM Global Model for Assessment of Competition for Land Use between
Agriculture, Bioenergy, and Forestry
GTAP Global Trade Analysis Project GUI Graphical User Interface HPD Bayesian Highest Posterior Density IIASA International Institute for Applied System Analysis IFPRI International Food Policy Research Institute IMPACT International Model for Policy Analysis of Agricultural Commodities
and Trade JRC Joint Research Centre JRC-IPTS Joint Research Centre - Institute for Prospective Technological
Studies NMS New Member States NUTS Nomenclature of Units For Territorial Statistics OECD Organisation for Economic Co-operation and Development PCE Private consumption expenditure deflator PRIMES EU-wide Energy Model UN United Nations USDA United States Department of Agriculture US United States
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1. Introduction
The CAPRI modelling system is designed for comparative static analysis. In its
essence it means comparing alternative scenarios to a given baseline. Constructing
a baseline, therefore, is an integral part of any policy impact analysis with CAPRI.
Building a baseline involves two major steps:
(1) A possible future state of the (global) economy needs to be projected and
translated into a set of consistent model parameters. This also includes
projected values for model-endogenous variables.
(2) The modelling system needs to be calibrated to the projection, i.e. the model
reproduces the above set of projections including of course the endogenous
model variables.
The CAPRI modelling system consists of several interlinked sub-modules that might
follow different calibration approaches. The supply and demand equations of the
global market model, for example, are calibrated by (1) shifting them to projected
levels and (2) trimming the elasticities by using econometric estimations and
imposing regulatory conditions. The regional supply module on the other hand is
calibrated following Positive Mathematical Programming (PMP) techniques, first
formulated by Howitt (Howitt 1995) and has been further improved over the last
decade (Heckelei and Britz 2005).
This report guides the reader through the CAPRI modelling system in order to
complete the two steps from above. The text always refers to the relevant code
implementation in order to give the reader further insights and to help him putting
theory into practice. But given the size of CAPRI this objective can only be fulfilled to
a limited extent and only with the aim of giving the reader a good starting point for his
own further investigation.
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2. Overview of the CAPRI modelling system
CAPRI is a comparative static partial equilibrium model for the agricultural sector
developed for policy and market impact assessments from global to regional and
even farm type scale. It consists of two main components: a set of mathematical
programming models covering the agricultural supply of most European countries
(hereinafter referred to as the 'supply module') and a global equilibrium model for
agricultural commodity markets (hereinafter referred to as the 'market module').
(Figure 1) (Britz and Witzke, 2012).
The market module is a comparative-static, deterministic, partial, spatial, global
equilibrium model covering about 75 countries or country aggregates. Based on the
Armington approach (Armington, 1969), products are differentiated by origin,
enabling to capture bilateral trade flows. The EU is split in three trading blocks:
EU15, EU10 and BUR1). EU trade relations are simulated at this geographical
aggregation level. On the other hand, each of the EU Member States has an own
system of behavioural functions, i.e. supply and demand functions (Britz and Witzke,
2012). The market module is defined by a system of behavioural equations
representing agricultural supply, human consumption, multilateral trade relations,
feeding balances and the processing industry; all differentiated by commodity and
geographical units.
The supply module is composed of separate, regional and farm-type, non-linear
programming models. The regional programming models are based on a model
template assuming profit-maximizing behaviour under technological constraints, most
importantly in animal feeding and fertilizer use, but also constraints on inputs and
outputs such as young animal, land balances and policies (e.g. set-aside) (Jansson
and Heckelei, 2011). The supply module currently covers all individual Member
States of the EU-27 and also Norway, Turkey and the Western Balkans broken down
to about 280 administrative regions (NUTS 2 level) with up to 10 farm-types in each
of the NUTS 2 region (in total 1823 farm-regional models) and more than 50
agricultural products.
The challenge is calibrating these two modules relying on different modelling
paradigms to the same projected state of the economy. The calibration of the market
module requires that market equilibrium conditions are satisfied at calibration point.
The quantities of demand and supply functions are dependent on prices and both
quantities and prices represent the point to which market module is calibrated. On
1 BUR includes Bulgaria and Romania, the two new Member States joined in 2007
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the other hand, the regional programming models in the supply module should be
calibrated to the projected land use and animal production at the same prices the
market module is calibrated to. The calibration requires that first order optimality
conditions (marginal revenues equal to marginal costs, all constraints feasible) hold
in the calibration point for each of the NUTS 2 or farm-type models. Positive
Mathematical Programming (PMP) is applied to close the difference between
marginal revenues and marginal costs in the calibration point by introducing non-
linear terms in the objective function to capture other unaccounted costs (e.g. labour,
capital) such that optimality conditions are satisfied at defined levels of decision
variables (Britz and Witzke 2012).
Figure 1: Structure of CAPRI model
Source: Britz and Witzke (2012)
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3. CAPRI building blocks
Calibration of CAPRI is split in several tools consistently interlinked between them
with the aim to facilitate data manipulation, generation of projections of the future
state of agricultural economy, calibration to the AGLINK-COSIMO baseline and
exploitation of results. The building bocks of CAPRI relevant for the calibration
process can be split in four tools: (1) database tool, (2) projections, (3) calibration
and scenario simulations, and (4) exploitation of results. The interlinkage between
these tools and their specific components are depicted in Figure 2. The main
database tools include MS level database CoCo, regional database CAPREG, and
global database for world regions. CoCo and CAPREG databases alongside other
support data (primarily coming from AGLINK-COSIMO and PRIMES) are key inputs
into the trend projection module (CAPTRD). Calibration is done within the CAPMOD
module combining information from CAPTRD, global database, and policy data. User
friendly results are provided primarily through the Graphical User Interface (GUI) in
form of tables, maps and graphs.
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Figure 2: Interlinkages between CAPRI tools
Calibration and
scenario simulations
Projections Database tool
CoCo MS data
Global World data
CAPREG Regional data Input allocation
DATA Eurostat,
FAO, FADN, etc.
CAPTRD Trend projections
CAPMOD
Support AGLINK,
PRIMES, etc.
Exploitation of results
Tables Maps Graphs
Policies
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4. Building a consistent dataset from diverse statistical sources
A key process necessary to be conducted prior to the baseline work includes
preparation and construction of data. Following the CAPRI structure, the main tools
needed for the baseline construction are:
1. COCO
2. CAPREG
3. Global
4. Policy data (policy module including CAP, trade policies, biofuel mandates
etc.)
The most important source of information for CoCo is EUROSTAT. This in itself
creates a consistency in terms of data definitions for EU Member states and selected
European countries (which would not be the case using national data sources). Other
supporting data sources include: FADN-based estimations (e.g. costs), expert info,
farm practice books, FAO, etc. For specific sub-modules (e.g. biofuels, GHG-
emission accounting, land-use) additional sources are needed as they cannot be
retrieved from standard statistical sources. For example, data for biofuels are
collected from European Biodiesel Board (EBB), European renewable ethanol
(ePURE), PRIMES model database, FO Licht’s World Ethanol and Biofuels Report,
COMEXT trade data, AGLINK-COSIMO model, etc. The data base for land use is
based on information on land use classes from various sources such as Corine Land
Cover (CLC), Farm Structure Survey (FSS), FAO, etc. For the NMS additional data
are included such as national data, FAO and Eurostat contractor Ariane.2 The use of
various sources for building CoCo is to impose completeness and consistency of the
final database in terms of temporal resolution and coverage of all relevant variables.
However, choice of particular database is done in hierarchical steps by giving
preference to key statistical sources (e.g. Eurostat). If data in the first best source
(e.g. Eurostat) are unavailable, then second best sources are used to fill the gaps for
missing data. To combine the various data sources and to ensure consistency of the
final database, Bayesian Highest Posterior Density (HPD) approach is applied. The
main principle of the HDP is to ensure minimal deviation of the estimated values from
the support data, as constructed from the EUROSTAT and non-EUROSTAT
statistical sources, subject to consistency constraints (e.g. closed market balances,
perfect aggregation from lower to higher regional level) (Britz and Witzke 2012).
2 For more detailed description see Britz and Witzke (2012).
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CAPREG is the regionalized version of CoCo with many important 'add-ons'. The
CAPREG database is broken down at NUTS 2 level or to the farm-type level inside
NUTS 2. The main data source is REGIO domain of EUROSTAT. The FADN and
FSS databases are the primary sources used for further disaggregation of NUTS 2
data to farm-type level. However, due to gaps in regional EUROSTAT data and
because of the relatively high aggregation level used especially in the field of crop
production, additional sources (e.g. European Fertiliser Manufacturer Association
fertiliser data available from FAOSTAT), assumptions and econometric procedures
(e.g. HPD, panel data estimators) are applied to close data gaps and to disaggregate
data to NUTS 2. The key concept in building the CAPREG data is to obtain
regionalised data at the NUTS 2 and farm-type levels by preserving the consistent
and complete national data base CoCo. Thus, the aggregation of CAPREG data over
regions and farm-types must match the national CoCo values (Britz and Witzke 2012;
Gocht and Britz 2011).
One of the most important additional calculations in CAPREG is the input allocation
module (e.g. fertilizer, nutrient balances, feed, labour) which distributes physical
quantities or monetary values of inputs applied to specific agricultural activities. Other
'add-ons' needs include herd sizes and yields disaggregation at regional level (Britz
and Witzke 2012).
The global database builds a consistent set of (macro)economic data for world
regions. It includes data on supply utilisation accounts, bilateral trade flows, as well
as data on trade policies (Preferential Agreements, Tariff Rate quotas, export
subsidies) and domestic market support instruments (market interventions, subsidies
to consumption). Its main statistical sources (for historical data and projections) are
FAOSTAT, AGLINK-COSIMO model, Agricultural Market Access Database (AMAD),
GLOBIOM (IIASA) and IMPACT (IFPRI). The primary use of the global database is in
the market module where global agricultural markets are modelled (Britz and Witzke
2012).
The primary focus of CAPRI is to asses the impacts of CAP policy instruments. For
this reason, the modelling of EU policies is more detailed and comprehensive than
for other regions. Policy data in CAPRI are compiled from various sources. For CAP
policies, the main sources are EU regulations and European Commission
documents. Both first Pillar 1 measures as well as major ones from Pillar 2 (i.e., Less
Favoured Area support, agri-environmental measures, NATURA 2000 support) are
included. For non-EU countries policy data are extracted primarily from AMAD
database and AGLINK-COSIMO model and include mainly market instruments such
as applied and scheduled tariffs, tariff rate quotas or bilateral trade agreements.
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5. Baseline process of DG AGRI
DG AGRI annually constructs an outlook for the medium-term developments in
agricultural commodity markets in the EU. This outlook permits a better
understanding of the markets and their dynamics and also contributes to identify key
issues for market and policy developments. Furthermore, the outlook serves as a
benchmark for assessing the medium-term impact of future market and policy issues
(hence we refer to it as ‘baseline’ in the following). The model used for the outlook
projections is a specific version of AGLINK-COSIMO, a recursive dynamic partial
equilibrium model with global coverage. The baseline construction process always
tries to build on the latest available market and policy information. Projection results
are presented in balance sheets for the main agricultural commodities, with detailed
results for the EU. The commodities covered include cereals, oilseeds, sugar, rice,
biofuels, meat and dairy markets (Fellmann and Hélaine 2012).
Figure 3: Flowchart of the baseline construction process
Source: M’barek and Londero (2012)
OECD-FAO Outlook Short-term – DG AGRI
First draft of baseline Macro-economics
Preliminary baseline
Baseline week (discussion with DG AGRI market experts)
Outlook workshop Uncertainty assessment
Calibration-CAPRI
Final baseline Calibration of CAPRI and ESIM
Publication
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The process of the DG AGRI’s baseline construction is depicted in Figure 3.3 The
starting point is the latest available version of the AGLINK-COSIMO model, which
was used for the OECD-FAO Agricultural Outlook4 of that year. The EU module of
AGLINK-COSIMO is then updated and optionally further extended in order to answer
EU-specific research questions (e.g. income module for EU farmers).
An in-depth discussion of the first results takes place between the modelling and
market experts of DG AGRI and the JRC-IPTS during a ‘baseline week’ in
September. After further adjustments, the projections are presented in October at the
‘Commodity Market Development in Europe – Outlook’ workshop, organised by the
DG AGRI and JRC-IPTS. In order to identify and quantify the potential variability of
the market projections, the results of additional scenarios with alternative
assumptions are also presented during the workshop. The workshop gathers high-
level policy makers, modelling and market experts from the EU, the United States
and international organisations such as the FAO, OECD, FAPRI and The World
Bank. The workshop provides a forum to present and discuss recent and projected
developments in the EU agricultural and commodity markets, to outline the reasons
behind observed and prospected developments and to draw conclusions on the
short/medium term prospects of European agricultural markets in the context of world
market developments. Special focus is given to the discussion on the sensitivity of
the projected market developments to different settings/assumptions (regarding for
example macroeconomic uncertainties, biofuel policies, specific drivers of demand
and supply, etc.). Suggestions and comments made during the workshop are taken
into account to improve the final version of the outlook, which is then published in the
report ‘Prospects for Agricultural Markets and Income in the EU’ in December each
year5.
3 More detailed information on the general baseline construction process is given in Nii-Naate (Ed.)
(2011).
4 The OECD-FAO Agricultural Outlook 2012-2021 is available online: http://www.agri-outlook.org/
5 Latest available report can be found here: http://ec.europa.eu/agriculture/markets-and-
prices/reports_en.htm
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6. Projections of the future state of the economy
As mentioned in previous section, CAPRI does not generate its own baseline, but the
main objective is to calibrate it primarily to the DG AGRI baseline generated by
AGLINK-COSIMO model (section 5). However, AGLINK-COSIMO provides baseline
results only at EU15, EU12 and EU27 aggregate level as well as it does not cover a
full set of non-EU regions and activities available in CAPRI. For this reason, CAPRI
needs to supplement AGLINK-COSIMO with internal projections for regional level
and for activities not covered by AGLINK-COSIMO model. At the same time,
projections from other sources (e.g. FAPRI, FAO) are used to supplement AGLINK-
COSIMO data mostly for the non-EU countries.
CAPRI projections are generated in two separate processes: (i) within the trend
projection module (CAPTRD) and (ii) as part of the calibration of market module. The
latter refers to the data balancing problem, aiming at creating consistent commodity
balances at the global scale. The estimation framework in CAPTRD guarantees that
the internal projections are as close as possible to the AGLINK-COSIMO baseline.
The regional supply module is calibrated to the CAPTRD projections. On the other
hand, the market module is calibrated to the outcome of the data balancing problem
of the global commodity markets.
6.1. CAPTRD
CAPTRD (defined in captrd.gms) projects commodity balances and prices for the EU
countries, Norway and Western Balkan (countries covered by the supply module).
Trend projections are derived by minimizing weighted squared deviation of trend
values to support points. The weights reflect the variation of the error terms in the
historical trend model and/or defined by the ‘trust level’ of the supports. The
optimization is subject to a set of consistency constraints. CAPTRD integrates a
multitude of external information sources and historical trends derived from CoCo
and CAPREG. The optimal values can therefore be interpreted as the ‘closest’
consistent projections to a set of external projections/forecasts and historical trends.
More specifically, the a-priori information sources for defining the support points are
typically forecasts and projections from national and international organizations (e.g.
AGLINK-COSIMO baseline, OECD market outlook, EC prospects for commodity
markets). There exists also a built-in possibility in CAPRI to rank the information
sources by their ‘reliability’, the so called ‘trust level’. This involves assigning
appropriate weights to the bits of a-priori information in the constructed Bayesian
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estimator. This issue is highly relevant in applied research (e.g. in policy impact
analysis) because the uncertainty in projecting different model parameters varies to a
great extent. For example, experts might express a high trust with regard to the
outlook on cropping areas at country level, contrary to e.g. the net trade position
which typically shows big variation over time and therefore difficult to predict.
Consistency constraints imposed during the generation of projections link the
different information sources by following some logical rules. Constraints ensure
consistency of projections between different activities and different regional levels. A
simple example is the area balance that links utilized agricultural area and cropping
areas, simply stating that summing up the land use of agricultural activities gives
back total utilized agricultural area. The consistency constraints are fully described in
Britz and Witzke 2012.
Overall the estimation framework in CAPTRD guarantees that projections are as
close as possible to the AGLINK-COSIMO baseline. As AGLINK-COSIMO provides
projection results only at EU15 and EU12 levels, these values are used to scale
proportionally the CAPRI projections at lower aggregation level such that they are
consistent with the AGLINK-COSIMO baseline. These CAPTRD projections are then
used as the targets for the simulation year to which supply module is calibrated.
6.2. Projections of global commodity balances
Projections of global commodity balances, trade and prices for all market regions are
run simultaneously with the calibration (see section 7). Wherever possible, the
AGLINK-COSIMO baseline is used to generate global projections. This is
complemented by other external information sources: Supply and Utilization
Accounts, trade matrices and projections from the FAO; Longer term projections from
GLOBIOM (IIASA) and IMPACT (IFPRI); and biofuel related projections from the
PRIMES energy model. Projections on commodity balances, trade and prices for all
market regions are made consistent simultaneously. Technically, the data balancing
problem is solved by the ‘arm\data_cal.gms’ module.
CAPRI has a separate global data preparation module (global.gms) that collects
information from different sources and converts it to the format accepted by CAPRI
(model specific product definitions, variables etc.). The module constructs a global
database for the base year that serves as the basis of projections until the simulation
year. The database is stored in a set of .gdx containers (under the folder
results\global):
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fao_agg_04.gdx: (1) FAO trade matrices (trade flows) and commodity balances, (2) biofuel-related balances, trade and parameters, (3) technical parameters (elasticities) from the World Food Model
tc_04.gdx: transportation cost estimates, the estimation procedure is integrated in global.gms and is based on the difference between cif and fob prices
tariffs.gdx: applied and bound rates for specific and ad-valorem tariffs, compiled mainly from the AMAD database (aggregated from HS6 level)
f2050_impact.gdx: IMPACT model results mapped to the CAPRI nomenclature
longrun_info_fac.gdx: long-term projections, currently until 2050, compiled from different sources, including PRIMES, IFPRI, FAO, etc.
In a next step, the ‘arm\data_prep.gms’ module collects base year information and growth factors for the market module from various sources and stores it in the parameters DATA and p_growthRateMarketModelPos.
In order to get a consistent quantity and price framework for the market module calibration, a balancing problem for the global commodity markets (market balancing problem) is set up and solved in the ‘arm\data_cal.gms’ module. The balancing problem is defined in ‘arm\cal_models.gms’ and includes the following equations:
Balance identities for market balances, for the two-stage Armington demand system and for trade
Trade policy mechanisms for public intervention, specific and ad-valorem tariffs, tariff rate quotas, export subsidies and the entry price system of fruits and vegetables
Accounting equations along the supply chain, i.e. feeding, processing and biofuel production
Price linkages, i.e. prices derived from the equilibrium market prices (producer, consumer, cif, import and Armington prices); processing margins
The balancing problem is first solved for the base year. Based on the consistent base
year data, prices and quantities are shifted to the simulation year and the balancing
problem is solved again. The algorithm for the balancing problem keeps certain
variables fixed while gradually relaxes others in order to find a feasible solution.
These consistent price and quantity data are then used as the targets for the
simulation year to which market module is finally calibrated.
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7. Calibration of the CAPRI modelling system
As anticipated in section 2, the supply and market modules of CAPRI are
sequentially calibrated in simulation runs (Britz 2008). The calibration of the modules
against the given baseline data generated by CAPTRD and 'arm\data_cal.gms'
therefore requires a consistent calibration point for the supply and market modules
with respect to prices and quantities. However, reflecting the different structure of the
modules, the calibration is based on different principles (PMP approach versus
calibration constraints for a system of equations). The calibration is executed with the
CAPMOD module (Figure 2).6
The supply modules are calibrated following the methods of Positive Mathematical
Programming (PMP), first formulated by Howitt (1995) and improved substantially
over the last decades by addressing the early problems of parameter specification
and simulation behaviour. For a summary on the methodological developments in
PMP modelling, see Heckelei and Britz (2005) and Heckelei, Britz, and Zhang
(2012).
Technically the calibration happens in two consecutive steps both using the
CAPMOD simulation engine in ‘baseline mode’ (Figure 4). First the market model is
calibrated (‘Baseline calibration market model’ in GUI). The data balancing problem
of the commodity markets and the actual calibration of the parameters for the
behavioural equations are solved in one go. Then the supply model is calibrated to
target values including product balances broken down to activity levels, land use,
feed demand and producer prices (‘Baseline calibration supply models’ in GUI). Most
of the target balances are generated by CAPTRD (see section 6.1) while the
producer prices are derived from the same market prices that the market model is
calibrated to.
6 For more detailed hands-on on calibration see Annex 1.
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The communication between CAPTRD and the simulation engine of CAPRI is via a
set of .gdx containers providing the results of the trend projections (in the folder
\results\baseline):
AGLINK_for_capmod.gdx: growth factors for the unit values (prices) in the CAPRI supply modules, derived from AGLINK-COSIMO results
results_0420.gdx: full result set of the trend estimation procedure, including intermediate steps for debugging purposes.
trends_04_20.gdx: trend estimates for the commodity balances and prices (in the simulation year). This is a subset of the full result set stored in results_0420.gdx, containing only the part directly used later by the calibration process
Figure 4: CAPRI GUI with the two consecutive steps of the calibration
The results of the market model calibration are stored under ‘baseline\data_market*year*.gdx’. The unit value prices (UVAG) used in the supply model calibration are prepared in the ‘arm\prep_market.gms’ module and written to disk in the file ‘baseline\data_uvag*year*.gdx’. The internal communication of these prices between supply and market modules are done through the p_pricesInIters parameter.
All calibrated parameters are stored in a single .gdx container ‘results\simini\sim_ini_*geog.level*years*.gdx’. This data file contains all parameters necessary for initiating a simulation run. The .gdx is created with the module ‘capmod\create_sim_ini_gdx.gms’ which calls the data preparation, data balancing and market model calibration modules explained above.
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8. Validation of the CAPRI baseline results
After the calibration of the CAPRI model, the results need to be checked and
validated to ensure reliability and plausibility of the baseline. As CAPRI is calibrated
to the AGLINK-COSIMO baseline, first objective of the validation exercise is to check
deviation of CAPRI results from the AGLINK-COSIMO results. The desired outcome
is that CAPRI results are relatively close to AGLINK-COSIMO results. However, they
are not expected to exactly replicate them due to the fact that CAPRI is a significantly
more detailed model in terms of regional coverage and disaggregation, activity
coverage, EU supply representation, behavioural relationships and CAP policy
modelling. Given its higher complexity, the CAPRI model needs to take significantly
more interactions and micro and macro constraints (e.g. cost allocation, nutrient
balances, policies) into consideration during the calibration compared to AGLINK-
COSIMO, which might result in differences in baseline results.
Following this logic, the check of CAPRI baseline starts from the EU aggregate level
(EU27, EU15, EU-N12) according to the regional resolution available in the AGLINK-
COSIMO. This is followed by examination of results at MS level and blocks of other
non-EU countries for prices, production level (areas, number of animals),
supply/demand (production, domestic use), trade (export, import, net export) and
policies. Next important distinction is made between activities available in AGLINK-
COSIMO and non-AGLINK-COSIMO activities. The results for non-AGLINK-COSIMO
activities are evaluated based on expert opinion or other sources (e.g. the Outlook
workshop organised by the DG AGRI and JRC-IPTS, Agricultural Markets Briefs7).
A challenge for a CAPRI baseline validation is the high resolution of EU results.
CAPRI produces a huge quantity of regional (NUTS 2) and farm-type data which is
hard to check mainly due to the facts that AGLINK-COSIMO baseline does not
provide results beyond EU aggregate level and that no other comparable studies are
available that provide an EU wide baseline at regional or farm-type level. Time and
resources limitations further on typically do not allow checking all the disaggregated
results and derived indicators. Thus, a detailed expert validation must concentrate
more on selected indicators at representative countries, sectors, activities and policy
areas, typically chosen according to a specific particular study or scenario analysis
for which the baseline is constructed.
7 European Commission (2012): Prospects for the olive oil sector in Spain, Italy and Greece – 2012-2020',
Agricultural Markets Briefs. Available at http://ec.europa.eu/agriculture/analysis/markets/market-briefs/02_en.pdf
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9. Good management practices for the baseline
Bellow we list some of the main management practices that contribute to smooth
implementation of the CAPRI baseline calibration process:
Scheduling database updates (including data on agricultural and trade
policies)
Always run an ex-post calibration. This results in a full set of results for the
base year that can be later on directly compared to the baseline for the
simulation year)
Always run a baseline replication test to check that the calibration process
worked corrected.
Always run test scenarios and compare the model's output to the expected
model behaviour
For validation, compare the baseline results with other projections and model
outputs in a structured way
Document the underlying assumptions of the baseline
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10. Concluding remarks
The CAPRI modelling system is designed for comparative static analysis. In its
essence it is developed to model alternative scenarios to a given baseline.
Constructing a baseline, therefore, is an integral part of any policy impact analysis.
CAPRI does not generate its own baseline but the aim is to calibrate it as close as
possible to the DG AGRI baseline generated by AGLINK-COSIMO model.
Calibration of CAPRI is split in several tools consistently interlinked between them
with the aim to facilitate data manipulation, its calibration to the AGLINK-COSIMO
baseline and exploitation of results. It includes modules to derive a set of consistent
databases for the calibration (Figure 2):
Coco and CAPREG for countries covered by the supply module at national,
regional and farm type level
the global.gms module to compile a consistent database for the global
commodity markets
a projection tool (CAPTRD) which generates target points for calibrating the
supply module;
calibration and scenario simulation tool (CAPMOD) calibrates the CAPRI
model to target points and allows counterfactual scenario analysis;
and the Graphical User Interface (GUI) allows to extract and analyse results.
CAPRI is built of two main components: supply module following a template
approach and a global market module. Both modules rely on different data sources
and calibration approaches. The market module is defined by a system of supply and
demand equations. The calibration requires that market equilibrium conditions are
satisfied at the calibration point; supply and demand quantities and prices represent
the point to which market module is calibrated. The supply module captures in high
details EU production structure and with its constraints captures many aspects of the
Common Agricultural Policy. The template model of the supply side follows a Positive
Mathematical Programming approach assuming profit-maximizing behaviour subject
to technological, endowment, policy and agro-economic constraints.
JRC-IPTS has a regular calibration exercise which is executed in annual cycles in
cooperation with DG AGRI and results are published in a joint DG AGRI – JRC-IPTS
report ‘Prospects for Agricultural Markets and Income in the EU’. Other key use of
CAPRI baseline is for scenario analysis of various policies of interest to DG AGRI,
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JRC and other research institutions. Important is to note that CAPRI baseline is
usually recalibrated when a policy scenario is analysed in order to take into account
the specific needs of the analysed polices, update of data and improvement of
modelling of behavioural relationships relevant for the analysed polices.
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References
Blanco-Fonseca, M. (2010) Literature Review of Methodologies to Generate Baselines for
Agriculture and Land Use. CAPRI-RD project deliverable D4.1
Britz, W. (2011): An overview on the CAPRI model. Institute for Food and Resource
Economics. University of Bonn, http://www.capri-
model.org/dokuwiki/doku.php?id=capri:ts:TrainingMaterial
Britz, W.; Witzke, H.P. (2012): CAPRI model documentation, Institute for Food and Resource
Economics. University of Bonn, <http://www.capri-
model.org/docs/capri_documentation.pdf>
European Commission (2012): Prospects for agricultural markets and income in the EU
2012-2022. Available at http://ec.europa.eu/agriculture/markets-and-prices/reports_en.htm
Fellmann, T., Hélaine, S. (2011): Commodity Market Development in Europe – Outlook.
October 2011 Workshop Proceedings. JRC Scientific and Technical Reports, European
Commission. JRC 67918.
Fellmann, T., Hélaine, S. (2012): Commodity Market Development in Europe – Outlook.
Proceedings of the October 2012 Workshop. JRC Scientific and Policy Reports, European
Commission. JRC 76028.
Gocht, A. (2010b): Update of a quantitative tool for farm systems level analysis of agricultural
policies (EU FARMS). In: Dominguez, I. P., Cristoiu, A. (eds.). JRC Scientific and
Technical Reports (EUR24321EN), IPTS Seville, 92 pp.
Gocht, A. and Britz, W. (2011): EU-wide farm types supply in CAPRI - How to consistently
disaggregate sector models into farm type models. Journal of Policy Modelling, 33(1) 146-
167.
Howitt, R.E. (1995) Positive Mathematical Programming. American Journal of Agricultural
Economics 2. 77:329-342.
M’barek, R. and Londero, P. (2012): Commodity Market Development in Europe Outlook
workshop, 16/17 October 2012, Brussels.
Nii-Naate, Z. (Ed.) (2011): Prospects for Agricultural Markets and Income in the EU.
Background information on the baseline construction process and uncertainty analysis.
JRC Scientific and Technical Reports, European Commission, Luxembourg. Available at:
http://ipts.jrc.ec.europa.eu/publications/pub.cfm?id=4879
Heckelei, T. and Britz, W. (2005): Models based on Positive Mathematical Programming:
State of the Art and Further Extensions.Conference Paper. 89th European seminar of the
European Association of Agricultural Economics. Parma, Italy
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Heckelei, T., Britz, W. and Zhang, Y. (2012): Positive Mathematical Programming
Approaches - Recent Developments in Literature and Applied Modelling. Bio-based and
Applied Economics. 1(1): 109-124
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Annex 1: Calibration steps
Bellow are listed main technical steps that need to be performed during the CAPRI
calibration process. This work is usually done at IPTS (Sevilla), which developed a
set of GAMS routines that automate this process:
Before starting the calibration process the user needs to convert the original
AGLINK-COSIMO result set (which is usually provided in one table in text file
format) into a GAMS-readable format (.gdx file).
The files ‘convert_to_gdx.gms’ and ‘codes_not_mapped.gms’ have two
objectives, namely (1) convert the result set in the right format and (2) perform
certain tests on the code mappings between CAPRI and AGLINK-COSIMO.
The file ‘convert_to_gdx.gms’ prepares the AGLINK-COSIMO result sheet in an
appropriate format. It also checks for new definitions/codes in the AGLINK-
COSIMO nomenclature and outdated ones that are not anymore in use. This
checking is important because the AGLINK-COSIMO model is in continuous
evolution and its nomenclature can change with the new model releases. The file
‘codes_not_mapped.gms’ further checks if there are any AGLINK-COSIMO
definitions that are in use in CAPRI but not anymore maintained (or changed) in
AGLINK-COSIMO. The above GAMS routines create excel files for reporting.
If new definitions/codes are identified, a corresponding code update needs to be
performed in CAPRI in 'baseline\aglink(year)dgAgri_sets.gms' (e.g. create
'baseline\aglink2012dgAgri_sets.gms').
This is followed by the corresponding update of mappings between CAPRI and
AGLINK-COSIMO definitions/codes in
'baseline\aglink(year)dgAgri_mappings.gms' (e.g. create
'baseline\aglink2012dgAgri_mappings.gms').
Create files 'global\convert_aglink(year).gms' and
'global\bio_fuel_markets_aglink(year).gms' in '\gams\global' which allows CAPRI
to read AGLINK-COSIMO information in the global module (e.g.
'global\convert_aglink2012dgAgri.gms' and
'global\bio_fuel_markets_aglink2012dgAgri.gms'
‘global\convert_aglink.gms’ loads in the original AGLINK-COSIMO results;
convert them to the CAPRI nomenclature and does corrections on
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commodity balances. The converted AGLINK-COSIMO projections are
stored in the parameter p_dataMarket for further processing.
‘global\bio_fuel_markets.gms’ aims to compile a consistent data set for the
biofuel markets based on AGLINK-COSIMO and F.O. Licht8 information.
The module calculates balances for the down-stream sector of biofuel
production as well. Processing coefficients and extraction rates are
harmonized with the ones used in AGLINK-COSIMO. The results are
stored in the parameter p_bioDat.
Cross-check those implicit assumptions that are set manually in the market
balancing procedure but should be harmonized with the ones used in the EC
baseline. This mainly includes the policy assumptions (tariffs, WTO notifications,
institutional prices etc.). The relevant code snippets are in the ‘arm\data_cal.gms’
module.
Select the AGLINK-COSIMO version you would like to use in the following
screen, consistent with your calibration.
This triggers a change in 'gams\global.gms' by deactivating the includes for the old AGLINK-COSIMO baseline and by activating the new ones, e.g.:
$SETGLOBAL AGLINK aglink2012dgAgri
$SETGLOBAL AGLINK_scen aglink2012dgAgri
8 F.O. Licht’s World Ethanol and Biofuels Report provides statistical information and projections on global biofuel
production and use.
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If CAPRI is calibrated to AGLINK-COSIMO biofuel projections instead of
PRIMES, the following options need to be adapted in the GUI.:
This corresponds in the code to
Update the following file 'captrd\scale_biofuel_to_dgagri(year).gms' (e.g.
'captrd\scale_biofuel_to_dgagri2012.gms') and
Activate scale_to_dgagri in the file 'biofuel\bio_trends.gms', for example :
$SETGLOBAL ScaleToDGAGRI ON
$SETGLOBAL ScaleToDGAGRI DGAGRI2012
$ifi %ScaleToDGAGRI%==DGAGRI2012 $INCLUDE
'CAPTRD\scale_biofuel_to_dgagri2012.gms'
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Implementation of the calibration in the GUI:
Database update (this step might be skipped if national and regional data are not
updated):
CAPREG time series (only NUTS 2)
GUI: build database -> Build regional time series
CAPREG base year at NUTS 2 and afterwards at Farm type level
GUI: build database -> Build regional database
GLOBAL database
GUI: build database -> Build global database
Trend projections (CAPTRD):
Run trend projections at MS and then NUTS 2 level
GUI: Generate baseline -> Generate trend projections (choose MS and then NUTS 2)
Run trend projections at farm-type level (only if interested in farm-type calibration,
otherwise this step can be skipped)
GUI: Generate baseline -> Generate farm type trends
Update or adjust the ‘load_aglink.gms’ module if necessary; modify variable
bounds or equations of the trend models if necessary
The load_aglink module has the following functions:
Initialize set definitions (for AGLINK-COSIMO) and mappings between the
two models nomenclatures; Load in the raw data under the parameter
p_aglinkOri
Restrict the complete AGLINK-COSIMO result set to the EU and calculate
balance items that can be later on mapped one-to-one to the CAPRI
balances. This includes breaking down EU27 results to EU15/EU12 when
necessary; calculating missing balance items etc. This is done in the
parameter p_aglink.
The content of the p_aglink parameter is mapped to the CAPRI
nomenclature and stored under p_aglinkTrd. Some additional items are
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calculated, e.g. crop yields, balances for different intensity variants of
CAPRI activities (e.g. high yield dairying), demand for biofuel feedstock,
cow milk demand.
The results of the above calculations are saved under p_result and
p_aglinkUVAG. The first parameter is the general container for results, the
5th dimension indicates the source of the information; in this case it is
marked with ‘dgAgri’.
The AGLINK-COSIMO information needs also to be scaled in order to match the
base year values coming from the CoCo/CAPREG databases. This is done in the
sub-module scale_DG_Agri_baseline.gms. The results of the scaling algorithm
are stored under the flag ‘dgAgri1’ in the p_result parameter.
As already noted above, CAPTRD needs to derive its projections at regional and
farm-type level. That means that AGLINK-COSIMO results, which are given at
EU15/EU12/EU27 level, must be broken-down to more detailed geographical
levels. The trend models of CAPTRD do this job by also integrating further
information sources (CoCo, CAPREG, expert information). The trend models are
defined in equations.gms, the consistency constraints guarantee a consistent set
of projections at all geographical level.
Baseline calibration (CAPMOD):
MTR_RD Baseline calibration at country level with market module switched ON
GUI: Generate baseline -> Baseline calibration (choose MS)
MTR_RD Baseline calibration at NUTS 2 with market module switched ON
GUI: Generate baseline -> Baseline calibration (choose NUTS 2)
Run farm type with market module switched OFF (this step can be skipped if
farm-type calibration is not needed)
GUI: Generate baseline -> Baseline calibration (choose farm-types)
Run baseline scenario (CAPMOD):
Run TSTCAL.gms at country level and NUTS 2 level
GUI: Run scenario -> Run scenario (choose MS and then NUTS 2)
Recommended